Why AI agents are becoming mission-critical for enterprises
AI is steadily shifting from being a productivity enhancer to becoming a core operational layer. The impact can be seen no better than in the sphere of sales and customer-oriented roles, where timing, contextual understanding, and decision-making play key roles.
In an interaction with Express Computer, Sangeeta Giri, SVP & Chief Operating Officer – South Asia, Salesforce, talks about how AI agents are transforming business processes, making them productive and changing approaches towards customer engagement and business operations.
Experimentation to execution
It is no longer enough for artificial intelligence agents to engage in experimentation or prove concepts. They must be incorporated into business-critical processes for reasons of time efficiency.
According to Giri, this is simply a question of time. “If you look at the day-to-day life of a customer, they are trying to see how they could improvise on time. It’s all about time to market and how quickly you are able to revert back to your customer.”
To illustrate this, she points to large-scale operational challenges, such as mergers, where customer experience can easily break down without intelligent automation. “What we saw is that agents were able to handle complex workflows, gather the right information, provide timely updates, and resolve customer queries quickly. In many cases, up to 90% of queries were resolved autonomously.”
This ability to operate at scale, while maintaining responsiveness, is what is pushing AI agents into mission-critical territory.
Reclaiming time: The biggest productivity unlock
One of the biggest challenges in enterprise sales has always been the limited time sellers spend with customers. Administrative overhead, research, and internal processes often consume the majority of a seller’s day.
Giri highlights this gap clearly. “Less than half the time a seller has from when they clock in to work actually goes into meeting customers. A lot of time is spent on prospecting, creating data, learning, and internal processes.”
AI agents are now addressing this imbalance by automating large portions of this preparatory work. “The agents significantly reduce the amount of prospecting time. Sellers no longer need to manually search across multiple systems. A single query can bring together all relevant insights in minutes,” she says.
This shift is not just about efficiency but enabling better customer engagement. “With this information readily available, sellers are no longer going in blind. They have context, customer insights, and clear problem statements before they even meet the customer,” adds Giri.
Data overload to actionable intelligence
While enterprises have always had access to large volumes of data, the challenge has been making that data usable.
AI-powered bots are disrupting this trend through their capability to turn raw data into actionable intelligence. “The volume of data available is huge, but what matters is getting actionable insights. You can now ask for the top three things you need to focus on, and the system gives you exactly that.”
This ability to distill complexity into clarity is making sales more precise and outcome-driven. “The amount of manual effort has drastically reduced, and teams don’t need to take notes anymore because information is automatically captured and available for future use,” she points out.
Human and AI, a collaborative model
Despite the rapid adoption of AI, Giri is clear that the future is not about replacing humans but augmenting them. “Agents always work along with humans. It’s not that they work independently. We have to set guardrails and define what the agent handles and what humans handle.”
The division is straightforward: automation handles repetitive tasks, while humans focus on complexity and judgement. “Any repetitive or high volume tasks can be automated. The minute a task becomes complicated, it is done manually,” she explains.
Such a hybrid approach helps maintain efficiency and control, especially for tasks in regulated sectors.
Real world result within minutes, not hours or days
AI-driven processes yield results that are tangible and quantifiable in real world scenarios. Giri cites a real world instance in the financial sector with reduction in time required for loan processing. “Processes that typically took two to three days are now being completed in under 30 minutes. The system handles verification, underwriting, and data checks and only flags cases where manual intervention is required.”
This has a direct business impact. From a business perspective, faster processing improves conversion rates and makes organisations more competitive in the market.
Such use cases highlight how AI is not just improving efficiency but also driving revenue outcomes.
Rethinking skills and learning in an AI-first world
As AI becomes embedded into workflows, it is also reshaping workforce expectations and learning models.
Traditional training methods are no longer effective in a fast-changing environment. “Earlier, enablement programmes would run for a couple of days with teams sitting in a room. Today, we neither have the time nor the need for that,” avers Giri.
Instead, organisations are moving towards more agile learning formats. “We are creating bite-sized learning modules that are simple, focused, and take 20 to 30 minutes. They include examples, demos, and hands-on exercises.”
This shift is also influenced by changing attention spans and work patterns. “People today do not have the attention span to sit through long sessions. Learning has to be quick, contextual, and immediately applicable,” she adds.
AI adoption: Advantage to necessity
One of the clearest signals from the market is that AI adoption is no longer optional. However, there is a clear distinction between organisations that embed AI deeply into their processes and those that use it superficially.
“Organisations that make AI part of their core workflows will move faster, see better ROI, and drive stronger outcomes compared to those who use it as an overlay on legacy systems.”
While adoption maturity varies, Giri believes this gap will close over time. “It’s a matter of time before everyone catches up. We have seen this with every technology shift.”
The road ahead: More contextual and local
Looking ahead, the focus is shifting towards deeper, more contextual use cases. “We are building more and more use cases that are closer to the customer. We are going industry-specific and even region-specific, adapting to languages and local requirements.”
This localisation will be critical in markets like India, where diversity in language, behaviour, and business needs requires highly tailored solutions. “It’s a journey. There is still a long way to go because requirements are constantly evolving and innovation is continuous,” she reminds.
Conclusion
The rise of AI agents marks a fundamental shift in how enterprises operate. What began as a productivity tool is now becoming an execution layer that drives real-time decision-making, enhances customer engagement, and improves business outcomes.
As Sangeeta Giri highlights, the organisations that succeed will be those that move beyond experimentation and embed AI into the core of their workflows, transforming not just how they work but how they compete.
